Computational models of ethanol-induced neurodevelopmental toxicity across species: Implications for risk assessment

Institute for Risk Analysis and Risk Communication, Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98105-6099, USA.
Birth Defects Research Part B Developmental and Reproductive Toxicology (Impact Factor: 1.17). 02/2008; 83(1):1-11. DOI: 10.1002/bdrb.20137
Source: PubMed

ABSTRACT Computational, systems-based approaches can provide a quantitative construct for evaluating risk in the context of mechanistic data. Previously, we developed computational models for the rat, mouse, rhesus monkey, and human, describing the acquisition of adult neuron number in the neocortex during the key neurodevelopmental processes of neurogenesis and synaptogenesis. Here we apply mechanistic data from the rat describing ethanol-induced toxicity in the developing neocortex to evaluate the utility of these models for analyzing neurodevelopmental toxicity across species. Our model can explain long-term neocortical neuronal loss in the rodent model after in utero exposure to ethanol based on inhibition of proliferation during neurogenesis. Our human model predicts a significant neuronal deficit after daily peak BECs reaching 10-20 mg/dl, which is the approximate BEC reached after drinking one standard drink within one hour. In contrast, peak daily BECs of 100 mg/dl are necessary to predict similar deficits in the rat. Our model prediction of increased sensitivity of primate species to ethanol-induced inhibition of proliferation is based on application of in vivo experimental data from primates showing a prolonged rapid growth period in the primate versus rodent neuronal progenitor population. To place our predictions into a broader context, we evaluate the evidence for functional low-dose effects across rats, monkeys, and humans. Results from this critical evaluation suggest subtle effects are evident at doses causing peak BECs of approximately 20 mg/dl daily, corroborating our model predictions. Our example highlights the utility of a systems-based modeling approach in risk assessment.


Available from: Julia M Gohlke, Oct 06, 2014
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